{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe6480a1",
   "metadata": {},
   "source": [
    "# 2D数据类别划分\n",
    "采用KMeans算法实现2D数据自动聚类,预测V1=80,V2=60数据类别  \n",
    "计算预测准确率  \n",
    "采用KNN，Meansshift重做 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39b47ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.072345</td>\n",
       "      <td>-3.241693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.936710</td>\n",
       "      <td>15.784810</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.083576</td>\n",
       "      <td>7.319176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.120670</td>\n",
       "      <td>14.406780</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.711550</td>\n",
       "      <td>2.557729</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          V1         V2  labels\n",
       "0   2.072345  -3.241693       0\n",
       "1  17.936710  15.784810       0\n",
       "2   1.083576   7.319176       0\n",
       "3  11.120670  14.406780       0\n",
       "4  23.711550   2.557729       0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "097b76c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#定义x和y\n",
    "x = data.drop(['labels'],axis=1)\n",
    "y = data.loc[:,'labels']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "465a48c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1156\n",
       "1     954\n",
       "0     890\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c441fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#先看一下不带标签的数据\n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure()\n",
    "plt.scatter(x.loc[:,'V1'],x.loc[:,'V2'])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bc7fdff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure()\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y==0],x.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y==1],x.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y==2],x.loc[:,'V2'][y==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7b7b0acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#建立KMeans模型\n",
    "from sklearn.cluster import KMeans\n",
    "KM = KMeans(n_clusters=3,random_state=0)# n_cluster是分为几类，后面没听懂\n",
    "KM.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "24000dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "centers = KM.cluster_centers_ #找中心点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "60c515c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure3 = plt.figure()\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y==0],x.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y==1],x.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y==2],x.loc[:,'V2'][y==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f68bd8be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "#测试v1=80，v2=60\n",
    "y_predict_test = KM.predict([[80,60]])\n",
    "print(y_predict_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1279cc64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    1149\n",
      "0     952\n",
      "2     899\n",
      "dtype: int64 2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#计算准确率\n",
    "y_predict = KM.predict(x)\n",
    "print(pd.value_counts(y_predict),pd.value_counts(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ee0cb267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0023333333333333335\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y,y_predict)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e477f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#准确率偏低，可视化数据检查\n",
    "fig4 = plt.subplot(121)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y_predict==0],x.loc[:,'V2'][y_predict==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y_predict==1],x.loc[:,'V2'][y_predict==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y_predict==2],x.loc[:,'V2'][y_predict==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig5 = plt.subplot(122)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y==0],x.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y==1],x.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y==2],x.loc[:,'V2'][y==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()\n",
    "# 因为是无监督学习，所以归类没有矫正，的确分为了三类，但是类别没有对应正确"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "426f9f2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    1149\n",
      "1     952\n",
      "0     899\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 矫正类别\n",
    "y_corrected = []\n",
    "for i in y_predict:\n",
    "    if i==0:\n",
    "        y_corrected.append(1)\n",
    "    elif i==1:\n",
    "        y_corrected.append(2)\n",
    "    else:\n",
    "        y_corrected.append(0)\n",
    "print(pd.value_counts(y_corrected))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1746bd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.997\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y,y_corrected))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "821ccbe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_corrected 现在不是一个pd数据的格式，没法直接判断[y_corrected==0]，要转换\n",
    "y_corrected = np.array(y_corrected)\n",
    "fig6 = plt.subplot(121)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y_corrected==0],x.loc[:,'V2'][y_corrected==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y_corrected==1],x.loc[:,'V2'][y_corrected==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y_corrected==2],x.loc[:,'V2'][y_corrected==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig7 = plt.subplot(122)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y==0],x.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y==1],x.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y==2],x.loc[:,'V2'][y==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3040acc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.072345</td>\n",
       "      <td>-3.241693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.936710</td>\n",
       "      <td>15.784810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.083576</td>\n",
       "      <td>7.319176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.120670</td>\n",
       "      <td>14.406780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.711550</td>\n",
       "      <td>2.557729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          V1         V2\n",
       "0   2.072345  -3.241693\n",
       "1  17.936710  15.784810\n",
       "2   1.083576   7.319176\n",
       "3  11.120670  14.406780\n",
       "4  23.711550   2.557729"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用KNN，MeanShift重新实验,KMeans只需要x就可以不需要y，但监督式需要y\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3237d72c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7fc81e58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立KNN模型\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNN = KNeighborsClassifier(n_neighbors = 3)\n",
    "KNN.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "749f02e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "y_predict_knn_test = KNN.predict([[80,60]])\n",
    "y_predict_knn = KNN.predict(x)\n",
    "print(y_predict_knn_test)\n",
    "print(accuracy_score(y,y_predict_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5a073f5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    1156\n",
      "1     954\n",
      "0     890\n",
      "dtype: int64 2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.value_counts(y_predict_knn),pd.value_counts(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d326e096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig8 = plt.subplot(121)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y_predict_knn==0],x.loc[:,'V2'][y_predict_knn==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y_predict_knn==1],x.loc[:,'V2'][y_predict_knn==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y_predict_knn==2],x.loc[:,'V2'][y_predict_knn==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig9 = plt.subplot(122)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y==0],x.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y==1],x.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y==2],x.loc[:,'V2'][y==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e0a1479c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.84663454820215\n"
     ]
    }
   ],
   "source": [
    "#Meanshift算法 不需要设置分为多少类 需要设置圆形的半径\n",
    "from sklearn.cluster import MeanShift,estimate_bandwidth\n",
    "bw = estimate_bandwidth(x,n_samples=500)# x 原始数据集，n_samples 通过多少个样本去估算\n",
    "print(bw)#计算半径大概多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ddaa9b41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeanShift(bandwidth=30.84663454820215)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#建立MeanShift模型\n",
    "ms = MeanShift(bandwidth = bw)\n",
    "ms.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2a39d388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1149\n",
      "1     952\n",
      "2     899\n",
      "dtype: int64 2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y_predict_ms = ms.predict(x)\n",
    "print(pd.value_counts(y_predict_ms),pd.value_counts(y))\n",
    "#分类又不对，矫正"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aed86afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dUQ2luuo6mmc2s+y5ZQ6P0D6G5BAtf25h64GtrH97fVwvi2KPSahOcC5hTutTUaE6r1If2Vozep/1tUFeWHpJoYdkGdUJLnvmrZ3n2cY/OmaignXVda7OaEpl12pbySWk2nbyCvu6+0zjDyou4V1SbTt5BTNRwWKOSahtJZegb6m0PLrDUjOgYmRcipWDx+U0Shp9W2Xliys9109ax2zlUMxyGmrl4CIWzqinusKb/lqgKdIarZBKQE6j5Gma0kRVwBlhvkIw66RZSaujYpfTUM7BZXg17iCBh7doWUklKKdR8rTvavd03GFb1zYWnLrAU3Ia3pymFiltWzs80SLUjL5QmG+v3hbN0FIOoTTQ5TW8TH+4n9+9+Ttuu+C2onYIsaiVg4u4fcNOzzqGWEqlG5xCI5W8hpfQ01qLvb5BRzkHF1FKGTsl0g1OQXFn7GSKl7rBKefgIkotY6eUnGEpU8wZO9ngFWeonIOLKLWMnVJzhqVKMWfsZINXnKFyDi7CiwHaYMDPtbMnqvTVEqZpShO1FfY0lXISr3eDU87BZYyqCjg9hLzy+fPqS7UbnCKGpbMsd0l1BVacmde7walUVpfxwyum8W0PifA9/UYXQMl1g1PE0zSliba32mztCJdPDh9L3YDaJ3xRJ+AVZ5CIWjm4jIUz6qkNemf1oILOCp1fzv8lAVEctj2EsVZS9H4TLSUvoZyDC2n57DTy3/3WGVTQWRGLVyQ06qrrnB6C7Sjn4EIWzqj3RDGcCjorEjk8kHq7phjwUtA5Fco5uJR6l824fRkuZfxCqKCzIoliT/MUCE8FnVOhnINLuXH+VNdsLdXXBrlj0XSunT3R8mOGpFSOQZGE23o71FXXMXvs7IweUwqOAZRzcC0LZ9RzTQYXYzu5cf5Ubt+wk/s37bb8GBVrUBjRNKWJlo+3IFwy9Wme2cz7H71v+fyaihobR+MuHHcOQgi/EGKrEOL3kdujhRBPCCHeivwe5fQYC0Xb1g7mtD7F5KXtzGl9isaTR2c0W7eL76zeRkd3n+U4iKD0qr2NULat0b6rnXlr59FwXwPz1s4DYOWFKwn4nM9cWvrc0oykxL3QVtkqbqhzaAZeB0ZGbi8FnpRStgohlkZu3+TU4HKhfVc7q15exf7e/YytHkvzzGbTJWnb1g6WrXuVvlAY0JRLv7N6m6ULst0y35k+t8Sb1d5Z4Fnbbtvawe0bdrKvu49xKSTYdbluXZW1s7eTpc9pBXFBf5AQxl0Pg/4gfWH3pUF7IaBuFUdXDkKI8UATcE/M4QXAfZG/7wMWFnpc+UD/UnT2diKRdPZ2ppTzvX3Dzqhj0HGDY8gGv3DHloGTeNm29YmMvppMJcGeSq471cW/oqzCVbEJnWIPqGeC09tKPwO+D3EVJydJKTsBIr9PNHqgEOIGIcRmIcTmrq4u+0eaIUZfilRyvtkWiznlGFJlU4VLaOmdAs/attFExkyCPVuF0u5j3a7sAVEKKaw6jjkHIcTlwAEp5ZZsHi+lvFtK2SilbBwzZkyeR5c7Zl8Ks+PFFMDV6xfMdKC8pg+VKV63bbOJjNFxp2famQa+K/2VnhQJzAYnVw5zgM8KId4DHgIuEUL8FvhACFEHEPl9wLkhZo4efJMmc3qzL8uN86cmKZemw6nNGxF5b2YLBLVw8J5txyZL+Ey2DY0mONmkrgZ8AWrK85MVZPY9NEMgmD9pvun9XmnkYwXHnIOUcpmUcryUchJwNfCUlPJa4FHgushp1wHrHRpixsTGGYxIVVm5cEZ9VLkUki/8+m19P782GKCqPDNnki+Ohoa48Xev0N1nHEzsMTleKnjNthNjDEbbhmbV8HrqqpncRMAXIOgfdipBf5DqQDU9Az15G38m9IX7ePjNh03v90ojHyu4IVspkVZgjRDiK8Bu4AsOjyctelZSqpS4uuq6lNlKEK9cmiobJDGzyQlCQ+YzsmLaIiswRWfbbVs7+N6aVwwdgk9oq8RU2UqgOQjd7lNl8OmTq+5j3fa9IQsMykEEwnDV4fQ2WSFxhXOQUj4DPBP5+0NgrpPjyYTEVD0zNl61MaPnTSVxbRQQ1PELQVjK6G+z++1C6SnFU8y2rU9CzOxlSMLPFk/PKG051lEkYpbZ5BM+huRQ0m87MXIMpaKppON0tlLRkypVT8cnfEmFQGYprVZIldkUlpJgwM/sKaOStqaCAT//tehcfrZ4esbxDSsItOY+qsbBG6SahMSek1i8aZTSagWzLRvdEQzJISr9lcw6aVbSOZX+ShZPXWxb+mtNeU3JaCrpKOeQI1b2IIfkkKWaB6sOJN22TV8ozAvvHDSY+2hHrMQ3rp09kZ8tnp5RbwnJcHMfRfFjJb1ar3FIVfNg1XlY2bLpD/cbNgyaPmY6N8++ORrfEAhqymvwi/hJUMAXoPXCVlovbM0o6F0VqCopxwDKOeSMFYP2CV/amodMiuayyWwC6AsNRb+4C2fU88LSS6ivDSY5Ef0iv3BGPdt+OI+fLZ5uOT1VNffxDlZiR34hUtY8ZFIwl4so36b9m2jf1U7TlCY2XrWR7ddtpypQRVjGjy00FGLVy6tomtLE80uep/XCVkvprqUUiNZRziFH0hl0pb/SdH+0s7eTCx+6MBqks1o0lzjzz4TEYiWzi3lHdx+Tl7Yz/daN3PrYDrqPhhhVFSCQRrtbBaO9Q7pJSDDgN41HdHT3cXPbqxkVzMVmNgkEPpHZ5Snxu2J2Qe/s7aThvgYuePACWl9qtZTuWkqBaJ2Sdg75iAMkGnRtRS015TVxDcdTdY3qPtbN8ueWm2Y67e/dbzjOhTPqWb6oj/ENdzDijKVUn9JK2citlsYc6xBSXcwl0N0X4tDREBI4dDSUsnmiCka7g3zFAGInIQKtuLE2GECgVcinm6D8dtNuOtIUzBmJ8m28aiMrL1zJyPKRho81I9EZpLqgSyQ9Az2WMqPKRFlJBaJ1hBdUBhsbG+XmzZszeoxRllGlvzJt0Ck2bVXPmqirruOi8Rfx7N5n2d+7n5qKGqSUHB44zNjqsVw0/iLWv70+KzmA2opa+gf74x5bJsoQCEIyvp5ADgXo77ySwcMzUj5nfW2QF5ZeAuQvLdYvBP+16FzPBqOFEFuklI2Fft1Mbdvo8wwG/JYaL7Vt7eDWx3Zw6KhmV7XBAJefW8fTb3RFU6ovPmNM0u2Ht3RkbD/1tUGWL+pL+g6a2bYV6qrr4rICrWYSpqO2opbnrn4up+dwK6nsumRXDplqH0FykZu+XdTZ28nqnauj8YLuY930DPREYwfr317PglMXZDzGMlGGlDJpnINy0PDLI3whKsZsACDgE1QbFMklzu4TZ4fZopr7uINMtnFiadvawY1rX4k6BtBWjfrsX48XJN5+eEsHnz8v88/94jPGGH4HzWw7Fp/BZcsozTRxVZ8tPcecKchzGlfUOThBptpHYC1t1Yj+cD+rd67O+HEjykdkbJgi0E19TFGSFWnl2JqKOa1PmW4FpELFGtxBJrpHsdy+YSehcOa7CH2hML/NoAmUztNvdPFRXeZBXr2YFLAkhx9bVzFv7byMejfolGK8AUrYOYytHmtoKEaGYKUC2g66j3XjE76MGoyMG1HHxsiWEaQupjPixvlTU24zBfwCZHyFtIo1uIdxtUFD527mvPXJQzYTglzo6O5j/MQx9ISsy0slbhtlmlraPLM55TZTwBdASsmgHIweK7XCt1hKdlvJKMvIyBDS6SXZTaaVoLkacuI2U20wwKiq4SDk7Vedy+1fODd6vx6YVFtK7sAow8jMebdt7eDG371ScMeg07X7YgKiwvL5udp24jZTTXkNtRW10eSRf5/z76y4YEX0fj2hpNTqG3RKduWgf+DplqbZbiXlG30FMbJ8JB8NfMSQQd7Q4qmL82LIVlYbyhm4E/1zsdKlreXRHSk1suzmWPcMRlaWc8L4P7G/d39BbDuVfEfsOYoSzlZKh1NbSakQiGj20x/f/WNUubK2opals5amNWqrrR0VyRRLtlI6Ym3ALd98wbB4X6BmGytfXJmxbWfSklcxTCq7Vs7BgHylwNlFYsqtlS9GLimOCm84Bzeo+abCyB6t2Ha2aekKlcqaMW7ZSjKjP9zPyhdXAtZlN7JNcXQN29fAT8+Gllrt9/Y1To+o6LAipOckfaEw31vzSrRoz6ptZ5OW7ipcatvKORiQi46KHuSym56BnpSyG7rz0Mk2xdEVbF8Dj30LevYAUvv92Ldc8yUqFnL9rE0awOWVsJRR7SWrtp1NWrprcLFtK+dgQC55zVZL8vOBvtw2G0fsDMsslbEo6hOe/BGEEi5soT7tuMIyuX7WhdqB1le0Vm3b7PtaFPUJLrbtlM5BCDFSCHGKwfEG+4bkPLmoQxYSfR/WjNhl9bxZHYw4tTVOh6lo6hN69mZ23AKHDx/mnXfeSTq+ffv2rJ/T7WSr5usE+7r7LNv2ReMvSrq/aOoTbLDtfGHqHIQQi4A3gIeFEDuEEP8n5u577R6Yk+j50IXYHsoFPUBnhj7zat/Vzu/33YkIdCME+Mq7Cdat4+qLu4ojGF0zPrPjaVizZg1nnHEGn//855k2bRp//etfo/ddf/31WT1nMZCLmm+hGVcbtGzb699ObsW94NQFxRGMzrNt55NUK4flwHlSyunAl4HfCCGujNxXgN1HZ2ma0sRzVz/H7LGznR6KKReNv4imKU2mTkyfeRkG2H0hXjj4G7uHmB/m3gKBhAtaIKgdz4LbbruNLVu2sG3bNv73f/+XL33pS6xbtw4go2r0YkTv4/FeaxMBl24qB/yCG+dPzd62gWf3PmvrGPNGnm07n6QyjzIpZSeAlPIl4GLgB0KIb4FrUqRtZcWmFYZdp9zCH9/9IwBLZy1NWe1d1AE7gIZFcMWdUDMBENrvK+7UjmfB4OAgdXWajPqsWbN4+umn+fGPf8ydd96JKETU1QXc3PYqIXvbMGdNmU9EV7TKtp0jVYX0YSHEKVLKdwCklJ1CiE8CbcC0QgzOadbsdD5jIBU9Az2s2LSCZ/c+S3+4P05CPDYfPBMdKdfSsChvX5iRI0fyzjvvcMopWjitrq6OZ555hoULF7Jjx468vIbbeeDFzMXyCkVfaCiaznr7hiDdQwsInrQRWdZNXUKtg7Jt+0i1cjgEjIs9IKX8CLgU+Ac7B+UWrHSIchpdKhyGG7AnFgpZ1ZEqFUaNGsW+ffvijh133HH88Y9/5Ne//rVDoyosDqpmWOLWx3ZE24uGDs/g8Fs3Mfj2f/D1U/5X2XaBSLVy2Aj8hxCiDlgNPCil3CalDAH3F2R0BSaxGrMY0Yt/Yr9AVnWkSoV58+bx/e9/n87OThYvXsySJUuYPn06gUCAa665xunh2UKidIrbie0roaOnuMYmUSjbto+08hlCiJOBqyM/lcCDaI7irZxeWIgJwP8FxgJDwN1SylVCiNFozmgS8B6wSEp5KNVz5UNiwO2SGZkgEGy/zrspmfni/fff56GHHuKhhx6iv7+fJUuWsGTJEk477bSkczORz3CbbbtdNiMTBPBuq7rw54uc5DOklO9LKX8ipZwBfBH4HFqKa64MAt+TUp4JzAa+IYQ4C1gKPCmlPA14MnLbdtwumZEJeVn1WC3pt6P0v0ByAieffDI33XQTW7du5YEHHuCRRx7hjDPOyMdTu8q23S6bkQl5WfVYsS+7bNClUhlGpHUOQoiAEOIKIcT9wOPAm8Dnc31hKWWnlPLlyN8fAa8D9cAC4L7IafcBC3N9LSsUTXZDGvKy32q1pN+O0v8CygmEQiEee+wxrrnmGi677DJOP/10Hn744Zyf1222XRQSKRbIS9GmFfuyywZdLJVhhOm2khDi08ASoAl4CXgIaJNS9uZ9EEJMAp4FzgZ2SylrY+47JKUcZfCYG4AbACZOnHje+++/b/n1jJQezeS59QygYiAxSylrfnp2xIATqJkA33kt8/PseO0ceOKJJ3jwwQdpb29n1qxZXH311SxcuJDq6mrTx2Srylpo2zaSZXei01u+qc+XxLwV+7LLBgtg25mS7bbScuAvwJlSyiuklPfb5BhGAA8D35ZSHrb6OCnl3VLKRill45gxYyy/npnS40XjLzKUzCgWx5BXrAKCJXcAACAASURBVJb051L6b7S83r7G+Mtj9Tktctttt3H++efz+uuvR1cOqRxDthTatvXYQkekV0NHdx/L1r3KxWeMKRrZDNuxYrP5tmv9eAFsO5+YZitJKS+2+8WFEAG0L8/9Usp1kcMfCCHqInUVdYD1JrMWMFN6XL1zNTXlNVSWVRZMOC/f6I4OcuxmVTPeZIYzPrvzEtm+BtZ/A8ID2u2ePdD2NVLOVfIoJ/D000/n7bnMcMK2zWTZ79+0m2tmT6R9e6dhFlAxoDs6yLELoRWbzcWuH/vWsJBezx5YdwNs/S3sfSn1mFyIYwX0QitF/RXwupTyjpi7HgWui/x9HZAsnJIDqWILPQM99A/2u15TKRV50bG3WtKfben/4zcNOwadoTAMmVy4XCInYBWnbNsstiCB1S+ZzFqLiLz0H7Fis9natZHCKhLe/X8GxzN4XodwUl1lDvAl4BIhxLbIz2eAVuDTQoi3gE9HbueNdJk8/eH+ol056OQcXLda0p9N6f/2NdB3MLPxuEROIAMcse1UmTyhIVm0q4ZYcg6uW7HZbCUtzLaNUuFi2y65NqHtu9pZ+lxBMghdgdUevAUhcdltBeEHOaQtvefeEv9F2r5Gm6317DW+P48UQ5vQtq0dfHv1NptH5Dx+IQhLmb8gdT7YvkbbQspUVaFmgmvt2qW6jPbRNKWJqrIqp4dRMLqPdfNvL/xbUmtFRzBcdqdBhjFM+yuytMBCsHBGPdXl3g88hyMTWj0OoeswOcqTPyIrPVIX23Uq+QxP0r6rnYHE/W6PExoKJUlq2ELijOe0efDWxuHbVpbd+kpB+CKOIQa9Q1bDotQdtFy6TLebtq0dDAyWVnadkaSGLcTadjCSfdx3aHhmbyXjSPiTbRpca9cl5xxWvbyKQTno9DAKji1FfolfmIEj8RlIm381fG7PHjTxgzSzKzkELd3QUmN8v/4ldHEHLae4fcNOQm5X1LMBW4r8Utl2bMxMn9kHR6WPpckhTL8DLrTrknMOXqmEzpSR5SPz+4SJ8QNLQWYLFy7hg99/F9MvkZ72l226oYfxSiV0ptQEA/l9wkxtO9QXOTfN5Ce64jB4Pv0+F9l1ycUczLKVhMeb2x0dPJp73CG2wOeRr2UeP7CCDMPmX2P8JRPDaX8u7qDlFGbZSl7vX9Q7MJhb3CGxcO3xm7K0bUnKJpkDR2DwWOqncJFdl5xzMNJ/B3t7N8zZEeauuwZ5aOUgd901yJwdhRdB0+MOWZMYKDPaO82U4GhtHzYJs89CDu+7uriDllPcOH+qYSW0nQmJn9yzhXs3rKC97V+5d8MKPrlni30vZkIoLLOvfzAKAGeaah2H1GwxODr5rvAAhExEJvoi4rwusuuS21bSg7IrX1xJz0CP7a83Z0eYf/qDpDIS5hhzGP7pDxII88K0wmaW5LSllk2mUToy/RLWTIi/7dIOWk6xcEY9m98/yP2bdhekTdUn92yhedtaKsNa/cRJfd00b1sLwDMTzivACIbJekvNDrvOpt4hdtvIJXbt6ZVD+6525q2dR8N9DcxbOy+6rdI0pYmqQGHSWb/4zLBj0Kkc1I4XmpykvN0Q6D1tntMjcAVtWzuY0/oUk5e2M6f1qbgtlaff6CpY/8Lr//Z41DHoVIZDXP+3xws0gmGylvIutF0HTDS8XGjbnnUOZgJ7uoMwUmC1g+NN5NbMjueKT5h/pDlJeeccEMtk49vk3C33lnQdA5iL6+kOopDqq2P6jJUEzI7bRU5S3mZ27a/IfkBmBIJQZvK8Ox7J/+vliGedg5nAnr7vnuoimk8+NEkSMjueKyPLR1ImkncLF09dnFudw9xbwF+efNwwZpAFsXusZnNfGYZ1X9XSXF3eKMUuzMT19D13fwGjz11BYw0ys+O5YvTOaoMBVl55TvZ1DmZ2LYfghLw0fiIudtBn0viv76Dr7NqzzsFsf10/Xigp7gc+KehPuFb3l2nH7aD7WDeDcpCgP4hAUFddR+uFrdw8++bcnrhhEZSPSD5uOTCdZrNj7i2RNL691hxOiVZEm+2t68fDBZTDufesy+j3x6eR9vsD3HvWZba8Xuw7q68N8rPF09n2w3m5FcCZ2fVQCD7MqROyRs0EuPJu7e91N2ip2qlwkV171jmY7a/rx+uq6woyjhem+fnFZwRdI7Vmwl0j4RefEbYHo/vCfay8cCUbr9qYv8pos1lPrgSqoe3rmWdC6ZWjJYTZ3rp+vD4fbTQt8syE81g1/So+CNYyBHwQrGXV9KtsD0YLyK+mkpld55yRJ2D0FM0pZGLbLrFrz2YrNc9spuXPLXFbS7EtNI3ut4sXpvl5YZrtL5NEzpIZiXIYVqpAM8UXmXmayXWnww2B8gJy4/ypLFv3atzWUuyeu9H9dvLMhPMKnpkkIXfJjFjbNpJqyRkBky+Cd58la80lh/Gsc9AviontQPXjsfcXKjhdaCylrpopQG5fo83m9Yt2zx7Ap+3P5kubSvi158/WMUDJVUTrF8TEVqD68cT7vSqmYSl11apt59sxCB9U1mp9HLJ+DucFFD3rHEBzAKlmzvr9Fz50YdH3cDAibeqqUeeqx76l/f34TQYX7SFtpj/ipPzMbPLxpSzBiuiFM+pTzppj75/xo42e6OOQSNrU1YxtG6Ih71xXEnIo9xV23lcymePZmEMmLJ21lIAvz/osLqCztzOuviOJVAqQZsYd6tUuyEYVoIVG+LT93FwzPMz6/nqAH14xjYDfe/oZvcfSSGZkY9tILXhcaSL6WGgctmtPrxwyoaqsqiAV04UmZV/pbBUg130194HlAz3jLHZWmGllaaoZpguqVHOhbWuHptQa9t7mUndfKHVPaVPbTrPidYttg+N2XfIrB71YzouOQce0r7TZfr2uEFlMZJvhkWqGWcTEFst5lZQ9pYstFmWW4uqgXZe8czAqlvMihsFpMwXIYiWbOIiL9PPziVGxnBcxDUwb2bablZdT1V05ZNcl7Rzad7V7NlMpEYk0jj+UxXyBgqNTV3G6HpH5Hq3ZDLPYZp4JeHnFoPNZ3/P8pbLZfE890baLNnfLGbsu2ZiDvp1USsTFH470xu9JAgxG/rba0tN1SC0TJZPm7HNvSf4/FHlfiLatHVZ67hU1n/U9T2vgHqqI6Tyo76mDsW0HR+e/TqcgyOHtIKu2nQe7Lhnn0L6rPa7moW+wryS2kxLR4w9Ne/aZ70nOvQXWfyN/9QyFpO/g8AXAShBOP56JQ3EZeuBZr3s4OjDoaccA8P2yNVSJBPuM3VM3su2yYH7rdApJz57kuqNUtp0Hu3atcxBCXAqsAvzAPVLK1kweH+sMaipqODJwJNo7ulS2kszY37s/9Z6kbkBuytzIFivN2Quon5+rXUO8M6itCnCkfzDaO7oUtpMAxokPje9Itafed0hLVX38puJcQSTWZqSz7Rzt2pUxByGEH7gLuAw4C1gihDjL6uMT5bp1MTovE/AFDNVYjRhbPTb1nuT2NfD7b+dxdA7jkuByrnYNyZLdh46Goo6hVBBAT/mJxnfWjE+dhZeyzqEIsdG2XekcgFnA21LKXVLKAeAhYIHVB5dKBlJVWVVUebWqrMqSA4zqS5llKp02T9tSGjBpZ1iMuCe4nJNdQ2lkIZX7BaOqAgiMJcgl8NTgDJKyj/Q9dSPb9gW0Hs5FGUtLgY227dZtpXog9lPcC3ws9gQhxA3ADQATJ06Me7DVdpg15TVUBaqKdpup3F/OLeffQtOUJhrua7D0mAWnLogvhkvck3zyR8W5J2uGv1xzeD+ZPDxjDI6Gy37iRFwhrV1Datu22g6zNhigu684ZTMGwpJq4KeLp/Od1duS7v+s73kuG3oKROyKScC5X4z/TGNte6DXWysGsN223bpyMEpIjls7SynvllI2Sikbx4wZE3eilXaYlf5Kln1sGRuv2lgw+e58032sO9rdrqbCWsn/s3ufHb7RsAi+8xq0dGu/GxZ5b2blC8DL/zf+wtB3UFsdFV4mI61dQ2rbttIOMxjw0/LZaQWV7843h45qFdC1VcmyNobBaCS8tXH4ZqJtF216dgpstm23Ooe9QGw3+fHAPqsPbp7ZTKW/Mu5YwBegprwmug3T8vGW6Aza6PxioT/cT+tLrRwZOGLp/LSrKheoQeaVUK+xyFp4wIkq6JzsGjRJ7mAg/jMK+AW1QW0bpr42GO2MZnRuMdEXCnPMYAttnPi78QNS7b+7Z2sxf9hs227dVvorcJoQYjLQAVwNfNHqg9PJdac7XwhRsE5x+SATRdm0qyoXqEEWjMIHqnOya0gv2Z3u3KMDg0Wl0no0lPw93CdPYLyRg0jlAObe4o3sO6vkwbZd6RyklINCiH8BNqCl/P1aSrkjk+dIJ9ed6vwVm1aweufqTF6uKIhtdmRKzQTvbC0Fglpuu9lec4Fnk/mwa0gv2Z3q3Gt++RdeeKe4997/Y3CRVgAXu7WUrsCrYVHxprACJJY1FsC2XekcAKSUfwD+4MRrx+3Lu5xKfyUV/oq0woF11XXGq6fEhiinzYNXHkguIioGAtVQNTo+wA7xxUM6/nJHqqCdtGuATbuKZ+89GPBTUeZLCqw/OnQBhGB5+e8Yy9+NC7yMGv1c9pPkquFiofEftJhKAW3btc7BSaxmOzmNfsEHUrY8FQg2XrUx+Q4jWd9XHtCyPnY8UnyzrFAvMFordErM1oidNTqXreQ4YVkcNRH1ke0ygG8bZCw9OnQBj/VfwLutBrsDZnLVV9yp/RTjCmLzr7RVfQFtWzkHA8ZWj3V9emtddV3SBX/588sNYyWmcQYzWd/Nv8LVCpap0C8EuzfFz7RK1Bkk4hfC9Q6ivjbIC0svid6+9bEdhnES08wtM7te91Xt4nnso3wOt3AU2Lbdmq3kKGn35QuIQCRlUhnFDpqmNHHbBbelPze2O1TK2IK7LyApCfXB5l9H3p8c/lJ5qMNbtiz52IT0JxWI6nJ/UjZVMOCPrhh0fnjFNEvnRW07lV33HcytZ7nTFNC2lXMwoGlKE7UVtU4PA4AyUUbLx1uoq66LpuEuOHUBq15eRcN9DXEy3E1TmpLOjU3ZjS63dcPyNAnvzwMNfPLBioXnUF3ujvRWKSUrrzyH+tpgNA338+fVc/uGnUxe2s6c1qdo29rBwhn1Sefp6bpR4mzb6xTGttW2kglLZy1NuY9fKEJSm+XoW0i6bpQ+rsQ2oCmztIyW26WESzSWnObHnzuHZetedVyGQ09T1beQdN0ofVwd3X1xrUBTZmgp2877U6qVgwmxs3CniW3xaaQbpRfCpaXUL45eLITKAn0mbqRbVGhi23wa6UalbAUai7LtvD+lcg4paJrSxMarNtJ6YatlxVM70LOnUnWu6z7WndzlLZGUBuRxUyjyBj75ZuGMev5r0bkEfM46CF0rqm1rh6ncuCUZ8lS27bWqfzPBwTzj8StCfmia0sSI8hGOvf7Y6rGWOtfFrjAMMeyrq2N/RXjPe0HeevREXn+ojrcePZGe9wqk/aO3P1XZSnEsnFHPiEpnd5bH1Qaj20lmWFrhzL0F8ww7++NrhbVtOezwaibYZtvKOVik51jqIrNYRB7TQPVso9aXWtPGP9LWZzQs0gzJAXreC9L51xoGj5YBgsGjZXT+taYwDmKwhPei09CdhZRGttad+Dg94+jWx3akjH9YSr1tWISpE7BZCscR25bh4RWDTZMe5RwsYkXpVWf7ddtZPHVxVq9jJBAI1vSTLI2xYZE22ygwB7YfhwzHm5sM+ziw/Tj7X1xlKpliReVVxy8E77U2cc3sielPTiAY8HPN7IlJGUdAWq0ny+qyZnZt87aSY7Zts12rbCWLNM9s5t9e+DdCFnKk23e1s/7t9Zaf2yd8SClNBQLnrZ2X9jks6SbpGDUf9wWAIRiyJ4Nl8KjxF9TseN4p9YClCTfOn2o5cyksJW1bO3h4S0dGr1GfQhxwTutTKR9rWM9ghpFdB4Jaxf/W39jWp8RR2y7BTnCuRFpY3vqEL+NOdCPLR7LywpVsvGpjkmNIFYTWSapnSIe+vVQzARDa74U/B4s9IbKhrMr44mN2PO+oTCVDFs6o5/Pn1VvaKvILkVEnOgFcO3siLyy9xNAxpApCg9awKKmeIRVGdn3FnXD5HWBjzNBR27bRroWVC57baWxslJs3b447FgqF2Lt3L/39+alT+ODoB4QtzKqrA9X0hjJvsSkQ1FbWEiwbXkL3DfbR3d+NNNlL9Qlf2q2kyspKxo8fTyCQ3DQliZZa7Are6fuysctv4R+i7v/0UDPJ5phAIJhz0E4IsUVK2ZjHUVmiELa9v6efQQt9qEdU+Ok9Fs7IQnwCaqsCVJXHb1IcHRik+2gIs5f1C6hLs51U8rZts117dltp7969HHfccUyaNAmRh3zuob+nD2qNqhzFuBHjePPgm5a2nxLxCz9nHH9G9PabB9+kYqjC8FwhBONGjEtZyS2l5MMPP2Tv3r1Mnjw5/s7ta5IFu4KjbBMk078kB7Yfx+BRP2VVYU5s+Mh+xwDxe7MeyFjKt22H9qaOZwkEo6sD1I+q4o3OwwyEMwvwCgRjRwcZVVUePfZG52ECJs/jE4L6UfHnJ5LStn//Xdhyrxa0FX4473pthm1T9bRjtl0WhHU3aLZtQ2Das86hv78/b18e0ALFRhf8gC/A6aNPjzt2YvWJ7Duyz9I2VCxhGab7WHf0gp/KwaRzDKA5kOOPP56urq74O7av0VoJxu7B9h3Uvki+gG3aMzWT+grjDIzo2aMJrz1+U9GL8OXbtsv9PsMLfrnfxxl1I+OOnVRTScehPoYysG2JpOOQ9rnrF/xUDiadY4AUtv3770aEI/UXD2u3J38Cjv7dtipqR2xbn8jZZNuejjnk68sD2gU/8fmEEJxYfWLSubUVtYwbMY6AT1vu+oXf8lgO9B6I/q0/PpGAL2BZ+8nwdZ/8kXFwToahrJyiVWS1Qt9BT4jw5dO2T6qpxJfwfD4hOKkmuXXuqKpy6kcFKfdndukYkpIPeoa3wcweX+73pXUMOob/gy33Gp/83vNaYFrZtmU8u3LIN/rF+EDvAUJDIQK+ACdWn2h6ka6tqI27r/tYNx0fpc/yiF0tGK1AdIfUfazb8liSSJXhMJB5vKTo0LeZinj1kE/0i/EHPf0MhIco9/s4qabS9CI9qqo8et+howPRx6Uj9hyjFYjukGKfM91YkjBrcyvDmsy11wUn82jbnl455JvailpOH306006YxumjT097MR4xYjhDwujcjt0dLLxwYdyx2NVC4gok4Auw4tsrWL9uPfuO7OPdd99lyfwlfLrx0yxZvIQDHx3AEipzR6W2JjCqqpwz6kbSML6WM+pGpr0Y67atPy6Rjj27uXLu+XHHYlcLiSuQcr+P2276JusfWUfHoT52vfsu11zxKeZ/fAZLrr6aD7qPWHsjZjUNwl8iiq3kzbaVc4jQtrWDOa1PxUkF5xuzbSIdo22qRIdU7i+n51gPUkp++qOf8qWvfYk/vPQHjqs9jp//4ufWBjL3Fq2VYCK+gNZqsxQIjnJ6BAWjELadbpvJaJsq0SGVl/noOTrAkJSsWtnCtf/4zzz23BaOq6nh5/9zt7WBnHe98fFJF+DpLaVY8mTbyjkwLBXc0d2HZFgqOF9foiNHjjB37ly+MPcLfO6iz/HU48OFP+HBMMu/sZwrP3Ely766jPKwdtHesmULn/jEJzjvvPOYP38+nZ3DtQ7hoTBSSl58/kXmXaEVyC1YvIAn2p+wNqCGRbDgLi1DSSc4Wqt1KDPOjlIUJ4Wy7cWXfYLPf2oOT28Ybo8dDoe5+Tv/zBfmXcDyf/kyFQwCqW17cEgipeSlF57l000LAPjsVUt44vE0opI6l98BjV8ZXkEIv3b74C48v6WUZ5RzIEepYAtUVlbyyCOP8MrWV3j8icf5zx/+J1JKynxlvPv2u3z/W99n546dHF97PD//+c8JhUJ885vfZO3atWzZsoV/+Id/4Ac/+EH0+fw+P90Huzlu5HGUlWlho5PGnUTX/i6zISTTsAhuehdaerSfm97VjvUVTwP6nCiR91kw2962lcc3PsEdK25GSkm5X/DeO29x07f/hZ1/e40TRtVasu0yn6D70EGOG1kzbNt14+j6IIO2vZffAT88qNn1Dw9qt0tpGzFPtq0C0gzLBls9nilSSpYvX86zzz6Lz+fjQOcBTgifwIjaEUyYMIE5c+YAcO2113LnnXdy6aWX8tprr/HpT38a0GZgdXXDfSVqDCqZhRCU+fPwcdqYD+4qSiTuUnDb3t/JSYFj1Jx4XHa2XVWetPnjE4JAhtlRSZSKXUPebNsR5yCEuB24AhgA3gG+LKXsjty3DPgKEAa+JaXcYPd4xtUGDcv4MxElS8X9999PV1cXW7ZsIRAIMGnSpGh1q1F6rJSSadOm8Ze//MXw+aoD1Uw7eRofHf6IwcFBguVBwofCTKjPg6De3Fu0nGkvY2NvB2Xbudn2iIoyzj5lPB8d7mFwcJCqinLCRz5kwniLEhpmlIJdQ15t26ltpSeAs6WUDcCbwDIAIcRZwNXANOBS4OdC2N+p4xuXjKeiLN6QK8oE37gkPx64p6eHE088kUAgwNNPP837778fvW/37t3RL8qDDz7IBRdcwNSpU+nq6ooeD4VC7NixI+45R1WO4lOXfIrXn3md00efzroH17FgwYLcB9uwCHwW0waLkfJqu3s7uMq2v37xKVSUxX/NK8p8fP3iU/Ly/HbY9ujqCj419xLe3PQnzqgbySOrH8jdthsWxcfYvEie+5Y44hyklBullIORm5sA/Sq8AHhISnlMSvku8DYwy+7xNJ4K35x3PGNG+hHAmJF+vjnveBpPzc/zX3PNNWzevJnGxkbuv/9+zjhjWCLjzDPP5L777qOhoYGDBw/yz//8z5SXl7N27Vpuuukmzj33XKZPn86f//znpOf9yU9+wh133MGpp57Khx9+yFe+8pXUA9m+Bn56tqYz89OzjYtlfv9dGLJHvbLglFcnByaX77O1vsFttn3exFF84+JTGHNchWbbx1XwjYtP4byJ+cloKSrbHntOrm/XPYiYS3dwNFz5y+G4Yb5ewmnhPSHEY8BqKeVvhRD/DWySUv42ct+vgMellGsNHncDcAPAxIkTz4udsQC8/vrrnHnmmZbGsOPvO0zvm3bCtLjbORWfFZqjB+GjTl7ftZczn/0aHPsoXhrDSLjr1tHmhUTFxpW/zNuXJRvhPTfY9vYUukkN44ftNqfCs0ITsWvCA7y+5yBndrXDKw8kS3V71bZ9AS2zMA+2ncqubVs5CCH+JIR4zeBnQcw5PwAGgfv1QwZPZei9pJR3SykbpZSNY8aMyWmsqWQqYuk+1s2+I/uiVcyhoRD7juyz1Iin4HTvge73h2Uy+g4mayYZNQvxwpdHx6YVQjHZdiqZCp1DRwfoONQXrWAeCA/RcaiPQ0dduII8ehC6dw/b9dCgpp2UqJnkZdvOk2NIh20BaSnlp1LdL4S4DrgcmCuHly97gdio6nhgnz0jHCaVTEUsB3oPJInpSSk50HvAXauHowc1kTErxKb4/f679ozHCWzsdldMtp1KpkLng57+JCE9XQvJdauHnr1Yrlfwom3XTCiY7IsjMQchxKXATcBnpZRHY+56FLhaCFEhhJgMnAa8ZPd4jGQqjFRPzVRSs5HntpWPMsgJj017MxMtKzZszEZKh9ts20imIlH11EwXKVNp7oKQyezfa7ZdYLt2qs7hv4EK4IlIutsmKeXXpJQ7hBBrgL+hLcm/IWVh1oKJQnlGpJLtdhVW2yEmGlsxL7trJmgzxZrxtjZdt4DrbDtWKM+IVJLdRYuXbBvhiF074hyklKZ5QFLKHwM/LuBwLGN1+8lxhN/8yxAcrVVQGhlbqse5GRelKBajbVvZenIFR1M0ogpUQ9Vo8wlCMdt2ebX2vgrcsEpVSGdAprLdriNQraW7mXHe9fGNUqL4ABduMej098Q3PnnsW9rfSpLbEplKdjtGqu3SK36W+vMuRtsWPi3D0CHbLuJ1ozNkItsdK9ltxHvvvcfZZ5+d0etff/31rF2rZT/+93//N6eeeipCCP7+95gAtNkMKXTU+LiOmWhZ45czGmPBSXy/RpkqZljJjy8BspXsNsMW2061XZruYmlk25M/AUEXT+zkkLUMQzNytG3lHHSK8CIxZ84c/vSnP3HyySfH32Ekxw3WNFcSRcsmztZyyIsNKzo629doM7GePYAcnpkVwWefEV6xbVO7tpiZFmvbn/sf2PuSbT3TbaVAtq2cA9h+kdBljWfOnMk555zD+vXro/cNDg5y3XXX0dDQwFVXXcXRo9rsPpWssc6MGTOYNGlS8gseVxdfQQnZZzo8+SPb+u7ai0j/+Rm9t0xmZsWAl2zbyK6FKDG7hkLZtnIOYPtFQpc1fvnll3n66af53ve+Fw1q79y5kxtuuIHt27czcuRIS7LGaakarc2m9JmWryx7zZWilTqW6T8/s/dWtO/ZAC/ZdqJd+8u1gK1X7bq8GtPayQLYtgpIg+0XiURZ446ODj744AOArGSNLVE1WvsB6HkdzpybfM72NZqRpUoBdY3UsR9NzDSByZ+Ad/+f8UPSfX5m781Lct5es+1YuwY4+Lrxeels2zV2jSZ0aaRn1nC1SRCdgti2cg5g+0Ui37LGeUHfbtBnlWaZEHNviT/PMUyC7Ad3RWocsvj8jN6bgwV0tqBs29i2XWPXmAtdvrXRUdtW20qg/cMCCfr2ebxI2CFrnDNWthv02Veoj/T9d0VkGVxgevZm//k1LNK222omoBUaTbBbzrvwKNvWMNpKK7Pa08Kh3tMO27ZyDmD7RcIuWeM777yT8ePHs3fvXhoaGvjHf/xH64NKt90QF8iE9Ho2EgYdEGqrGZ/b59ewCL7zGrR0a7+95BhA2bbRcd22LWcqOaRcZDEQSwAACaxJREFU7bBtOy7ZnQ8aGxvl5s2b445lImvsdQz/Fz8922S5OkEzJLP73YS/HMpHmFd855FsJLvzgbLt1HjWthGA1MbskF2rlUOpkm656vovDyBlZPbn4RoFReYUvW1HHAM4atfKOZQq6ZarVjtY2t/p0vx1c6keVXiXXG1b+LXHOWHbwkfSNpZDdq2ylUqZhkXmy9V0ImV6py0nmrYHguZZJsWQv66wn2xtO7aDXEuNPWNLhTTReXLArtXKQWFMKkmC2JlYoWdXehN1s/F5qUZBYQ9mtiP82a2e84LQtJ9cZNfKOSiMMdu3vfKX8ZkPhZJBrpkQ30Td5hRNhYcxs53P/U/8aqOgtn23pv3kIrtW20oKY/QvSdoKapMinXyiZ5lkMz6FIhG32LbL7Vo5BxsZMWIER44cMb3/vffe4/LLL+e1114zPSeR66+/nssvv5yrrroqmmMeCASYNWsWv/jFLwgE8tiVLtW+rY7dlab6rMlMDkE5A0dQtp0jRWDXalspQvuuduatnUfDfQ3MWzuP9l3tTg8pLddccw1vvPEGr776Kn19fdxzzz2FHUBsBbUd+7N6bANKQ1rbJpRtZ4Gdtq3HNsDVdq2cA9qXp+XPLXT2diKRdPZ20vLnlrx9ieySNf7MZz6DEAIhBLNmzWLv3gJmNCRWUMsw+ALmmvsZI4ZjG6UgrW0TyrazwG7blkNFYdfKOQCrXl5Ff7g/7lh/uJ9VL6/Ky/PbLWscCoX4zW9+w6WXXpqX8VrCyLCHQtqXSM8vz2XGFZudUQrS2jahbDsLzGy7fIT1xkKp0G3b5XatYg7A/t79GR3PFLtljb/+9a9z0UUXceGFF+ZlvJYwM+BQL8yN9PNNVMe0SmJ2RilIa9uEsu0sMAtC9x3UsuWM7Nrnh6GE7CZfQGtGFNveNNa2XW7XyjkAY6vH0tmbvLQdWz02L89vp6zxrbfeSldXF7/4xS/yMlbLpNLDf/ym4SBbcJSmfhmrfwTxQbjT5mnyxGbZGXNvgbavx1dE+wIqbdUCyrazQPiN01iFPzkWIcPD+keQHFw2OpZSNlxo3wcXoJwD0DyzmZY/t8Qtvyv9lTTPbM7L81uRNT7//PMNZY3PP/98QqEQb775JtOmTYt73nvuuYcNGzbw5JNP4vMVeIdw7i3m1dF9B4cVL/sORuoj7o6/4GeajZFwoUm6rTBE2XYWmNU3yHD8xVyGh1cCuj0b2bWZrTcsgt2bYPOvGZbMkFrP9omzHc9YcjTmIIT4VyGEFEKcEHNsmRDibSHETiHE/EKMo2lKEy0fb6Guug6BoK66jpaPt9A0pSkvz2+XrPHXvvY1PvjgA84//3ymT5/Oj35UwEBWwyKtWtkKuQbZnvxR/NIctNsuCdwZoWy7iG07VQV1vgPIb23ELVpKiTgm2S2EmADcA5wBnCel/LsQ4izgQWAWMA74E3C6lKlLFZWscWps+19kFFMQmq58NrTUYqypn8NzZkgmkt3KtguHLf8LI7tOpedVxLbtVsnunwLfJ/4/swB4SEp5TEr5LvA22pdJ4UaM1C/NVhO5BNnMHuuSwJ0ByraLGTNVVzt0j1xs247EHIQQnwU6pJSvJASt6oFNMbf3Ro4ZPccNwA0AEydOtGmkirQkVnOazbpyCR4XUa9nZdsewaxKuYRs2zbnIIT4E2CUEvEDYDlgFJI3ijIa7ntJKe8G7gZt6W1yTlLGRKlR8G1DO7RhXKQ3A8q23YKybXuxzTlIKT9ldFwIcQ4wGdBnVuOBl4UQs9BmU7Frt/HAvmxev7Kykg8//JDjjz++ZL9EUko+/PBDKisrC/vCdmjDuERvBpRtuwFl2/ZT8G0lKeWrwIn6bSHEe0BjJGj3KPCAEOIOtKDdacBL2byO3py8q6srD6MuXiorKxk/3vn9y1JA2XZhUbZtL66qc5BS7hBCrAH+BgwC30iXzWFGIBBg8uTJeR2fQpEtyrYVxYbjzkFKOSnh9o+BHzszGoUifyjbVhQzSnhPoVAoFEko56BQKBSKJByrkM4nQogu4P20J9rHCcDfHXx9u/Dq+4LM39vJUsoxdg3GDIdtW33+xUfe7NoTzsFphBCbrUorFBNefV/g7feWL7z8P/Lqe8vn+1LbSgqFQqFIQjkHhUKhUCShnEN+uNvpAdiEV98XePu95Qsv/4+8+t7y9r5UzEGhUCgUSaiVg0KhUCiSUM5BoVAoFEko55ADQohLIy0f3xZCLHV6PLkihHhPCPGqEGKbEGJz5NhoIcQTQoi3Ir9HOT3OdAghfi2EOCCEeC3mmOn7cKJ9p9vxkm17xa6hsLatnEOWCCH8wF3AZcBZwJJIK8hi52Ip5fSYXOmlwJNSytOAJyO33c69wKUJxwzfR+QzuxqYFnnMzyOfbcniUdv2gl1DAW1bOYfsmQW8LaXcJaUcAB5CawXpNRYA90X+vg9Y6OBYLCGlfBY4mHDY7H2o9p3JlIJtF51dQ2FtWzmH7KkH9sTcNm37WERIYKMQYkukVSXASVLKToDI7xNNH+1uzN6HFz/HXPHa/8TLdg022bbjkt1FjOW2j0XEHCnlPiHEicATQog3nB5QAfDi55grXvuflKJdQ46fo1o5ZE/e2j66BSnlvsjvA8AjaEvQD4QQdQCR3wecG2FOmL0Pz32OecBT/xOP2zXYZNvKOWTPX4HThBCThRDlaIGfRx0eU9YIIaqFEMfpfwPzgNfQ3tN1kdOuA9Y7M8KcMXsfjwJXCyEqhBCTyaF9p4fwjG2XgF2DXbYtpVQ/Wf4AnwHeBN4BfuD0eHJ8L1OAVyI/O/T3AxyPlgHxVuT3aKfHauG9PAh0AiG02dNXUr0P4AeRz3AncJnT43fDj1ds20t2HRl3wWxbyWcoFAqFIgm1raRQKBSKJJRzUCgUCkUSyjkoFAqFIgnlHBQKhUKRhHIOCoVCoUhCOQcPI4R4JlGJUQjxbSHEz4UQfxRCdAshfu/U+BSKbFB2XRiUc/A2D6IVMMVydeT47cCXCj4ihSJ3lF0XAOUcvM1a4HIhRAWAEGISMA54Xkr5JPCRc0NTKLJG2XUBUM7Bw0gpP0Qrl9f1368GVktV+agoYpRdFwblHLxP7BJcX3orFMWOsmubUc7B+7QBc4UQM4GglPJlpwekUOQBZdc2o5yDx5FSHgGeAX6Nml0pPIKya/tRzqE0eBA4F63dIwBCiOeA36HNvvZm2nxcoXAByq5tRKmyKhQKhSIJtXJQKBQKRRLKOSgUCoUiCeUcFAqFQpGEcg4KhUKhSEI5B4VCoVAkoZyDQqFQKJJQzkGhUCgUSfz/oFnQxkvaUvkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig10 = plt.subplot(121)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y_predict_ms==0],x.loc[:,'V2'][y_predict_ms==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y_predict_ms==1],x.loc[:,'V2'][y_predict_ms==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y_predict_ms==2],x.loc[:,'V2'][y_predict_ms==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig11 = plt.subplot(122)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y==0],x.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y==1],x.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y==2],x.loc[:,'V2'][y==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d3f3f2ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    1149\n",
      "1     952\n",
      "0     899\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y_corrected_ms = []\n",
    "for i in y_predict_ms:\n",
    "    if i==0:\n",
    "        y_corrected_ms.append(2)\n",
    "    elif i==2:\n",
    "        y_corrected_ms.append(0)\n",
    "    else:\n",
    "        y_corrected_ms.append(1)\n",
    "print(pd.value_counts(y_corrected_ms))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3d402155",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_corrected_ms = np.array(y_corrected_ms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ad9e1cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig12 = plt.subplot(121)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y_corrected_ms==0],x.loc[:,'V2'][y_corrected_ms==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y_corrected_ms==1],x.loc[:,'V2'][y_corrected_ms==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y_corrected_ms==2],x.loc[:,'V2'][y_corrected_ms==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig13 = plt.subplot(122)\n",
    "label0 = plt.scatter(x.loc[:,'V1'][y==0],x.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(x.loc[:,'V1'][y==1],x.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(x.loc[:,'V1'][y==2],x.loc[:,'V2'][y==2])\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1a18deee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.997\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y,y_corrected_ms))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea372289",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
